class_docstr: Base class for classification models using neural network, based on PyTorch Lightning wrapper. The forward pass
  and loss computation must be implemented if new trainers inherit from this class. The basic workflow is defined
  in this class as follows. Every training/validation/testing procedure will call `compute_loss()` to compute the
  loss and log the output metrics. The `compute_loss()` function will call `forward()` to generate the output feature
  using the neural networks.
  
  Args:
      optimizer (dict, None): optimizer parameters.
      max_epochs (int): maximum number of epochs.
      init_lr (float): initial learning rate. Defaults to 0.001.
      adapt_lr (bool): whether to use the schedule for the learning rate. Defaults to False.
docstr: Compute and return the testing loss and metrics on one step.
docstr_tok: ["Compute","and","return","the","testing","loss","and","metrics","on","one","step","."]
code: def test_step(self, test_batch, batch_idx) -> None:
      """Compute and return the testing loss and metrics on one step."""
      loss, log_metrics = self.compute_loss(
          test_batch, split_name="test"
      )
      self.log_dict(
          log_metrics, on_step=False, on_epoch=True, logger=True
      )
  
imports: import pytorch_lightning
  import torch
  import torch.nn
  from sklearn.metrics import accuracy_score
  from kale.predict import losses
label_desc: Use image node_id 7 for calling the BaseNNTrainer obj's underlying member method code with example usage: obj.test_step(test_batch, batch_idx) without return types